Determining bipolar electrical activity

ABSTRACT

This disclosure provides one or more computer-readable media having computer-executable instructions for performing a method. The method includes storing geometry data representing a primary geometry of a cardiac envelope that includes nodes distributed across the cardiac envelope and geometry of a body surface that includes locations where electrical signals are measured. The body surface is spaced apart from the cardiac envelope. The method also includes perturbing the primary geometry of the cardiac envelope a given distance and direction to define the perturbed geometry of the cardiac envelope including nodes spaced from the nodes of the primary geometry. The method also includes computing reconstructed bipolar electrical signals on the nodes of the primary cardiac envelope based on the electrical signals measured from the body surface and the geometry data, including the primary and perturbed geometries of the cardiac envelope.

TECHNICAL FIELD

This disclosure relates to systems, computer readable media and methodsfor determining bipolar electrical activity.

BACKGROUND

Electroanatomical mapping is a broad term that covers several modes ofmapping for a body surface, such as the heart brain. Some examples ofcardiac mapping are endocardial mapping and epicardial mapping. Themapping can be utilized to generate an image, such as an isochronalimage, for displaying electrophysiological information. One type ofcardiac map is an activation map, which can be used to displayactivation time patterns on a surface of the heart.

SUMMARY

As one example, one or more computer-readable media havecomputer-executable instructions for performing a method. The methodincludes storing geometry data representing a primary geometry of acardiac envelope that includes nodes distributed across the cardiacenvelope and geometry of a body surface that includes locations whereelectrical signals are measured. The body surface is spaced apart fromthe cardiac envelope. The method also includes perturbing the primarygeometry of the cardiac envelope a given distance and direction todefine the perturbed geometry of the cardiac envelope including nodesspaced from the nodes of the primary geometry. The method also includescomputing reconstructed bipolar electrical signals on the nodes of theprimary cardiac envelope based on the electrical signals measured fromthe body surface and the geometry data, including the primary andperturbed geometries of the cardiac envelope.

As another example, a system includes memory and a processor. The memoryis to store instructions and data. The data includes electrical datarepresenting electrical signals measured on a body surface for at leastone time interval. The data also includes geometry data representing aprimary geometry of a cardiac envelope, including nodes distributedacross the cardiac envelope, and geometry of a body surface includingmeasurement locations for the electrical signals measured on the bodysurface. The processor is to access the memory and execute theinstructions. The instructions are to at least compute reconstructedbipolar electrical signals on to nodes residing on the primary cardiacenvelope based on the electrical data and the geometry data, includingthe primary geometry and a perturbed geometry of the cardiac envelope.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a system to determine and display bipolarelectrical activity.

FIG. 2 depicts an example of a bipolar signal calculator.

FIG. 3 depicts an example of a three-dimensional perturbation space thatcan be used for displacing nodes to steer bipolar electrical activity.

FIG. 4 depicts an example of an initial and a perturbed surface geometrythat can be utilized for computing bipolar electrical activity.

FIG. 5 is a flow diagram depicting an example of a method to determinebipolar electrical activity.

FIG. 6 is a flow diagram depicting another example of a method toreconstruct bipolar electrical activity on an envelope.

FIG. 7 depicts an example of a system to measure and display bipolarelectrical information in conjunction with an evasive procedure.

FIG. 8 depicts an example of a graphical map to display bipolarelectrical information.

FIG. 9 depicts another example of a graphical map displaying bipolarelectrical activity and direction across a geometric surface.

DETAILED DESCRIPTION

This disclosure relates to computer readable media, systems and methodsto construct bipolar electrical activity for a geometric surface, suchas corresponding to a cardiac surface (e.g., epicardial and/orendocardial surface). The geometric surface onto which the bipolarelectrical activity is reconstructed is spaced apart from locationswhere electrical signals are measured. For example, electrical signalscan be measured by body surface electrodes distributed across an outersurface of a patient's body and a geometric surface can correspond to asurface within the patient's body spaced radially inwardly from themeasurement locations. The approach disclosed herein provides a solutionto the inverse problem that enables bipolar electrical information to berecovered directly from the body surface measurements. For example, thesolution provides a linear mathematical relationship between thefunction of the body surface boundary condition and bipolar electricalsignals on the geometric surface.

FIG. 1 depicts an example of a system 100 that can be utilized toreconstruct bipolar electrical signals on a cardiac envelope. As usedherein, a cardiac envelope may refer to any two-dimensional orthree-dimensional surface residing inside the patient's body on to whichelectrical signals are to be reconstructed. For example, the surface canbe a virtual surface (e.g., a sphere) or a surface of an anatomicalstructure (e.g., an epicardial or endocardial surface). For example, thecardiac envelope can be represented as a mesh structure inthree-dimensional space that is registered in or can be registered intoa patient's anatomy. The mesh structure may include nodes distributedacross a geometry representing the cardiac envelope.

In the example of FIG. 1, the system 100 is demonstrated as a computingapparatus that includes memory 102 and a processor 104. The memory canstore data and instructions that can be accessed by the processor 104 toperform the methods and functions disclosed herein. Also demonstrated inFIG. 1 is a measurement system 106 that can measure electrical signalsfrom the body surface and store the measured electrical signals aselectrical data 108 in the memory 102. The electrical data 108 mayinclude real time measurements of electrical signals and/or previousmeasurements. For example, the measurement system 106 includes aplurality of electrodes distributed across a patient's torso (e.g.,thorax region) to provide the electrical signals according to electricalsignals provided at the locations where the signals are measured overone or more time intervals. Examples of a non-invasive sensor array thatcan be used as electrodes in the measurement system 106 are shown anddescribed in U.S. Pat. No. 9,655,561, which was filed Dec. 22, 2011, orin International patent application No. PCT/US2009/063803, which wasfiled Nov. 10, 2009, each of which applications is incorporated hereinby reference.

The memory 102 also stores geometry data 110. The geometry data 110 canrepresent locations where the electrical signals are measured by themeasurement system 106. The geometry data can also include informationrepresenting a geometry of a cardiac envelope. The cardiac envelope maybe a geometrical construct, such as a sphere or other three-dimensionalshape approximating the size of the heart or be smaller or larger thanthe heart. As another example, the geometry may be a three-dimensionalmesh structure configured to represent the epicardial surface of aheart, such as generic heart or the heart of the patient where theelectrical signals are measured. The body surface locations where theelectrical signals are measured by measurement system 106 are spacedapart from (e.g., circumscribing) the cardiac envelope. The geometrydata 110 and the electrical data 108 can be combined to provideelectroanatomical data (representing measured electrical activity andspatial position where measurements are made).

The geometry data 110 can be generated from image data that is acquiredusing nearly any imaging modality. Examples of imaging modalitiesinclude ultrasound, computed tomography (CT), 3D Rotational angiography(3DRA), magnetic resonance imaging (MRI), x-ray, positron emissiontomography (PET), and the like. Such imaging can be performed separately(e.g., before or after the measurements) utilized to generate theelectrical data 108. Alternatively, imaging may be performedconcurrently with recording the electrical activity that is utilized togenerate the electrical data 108.

The processor 104 is configured to compute reconstructed bipolarelectrical signals on the cardiac envelope based on the electrical data108 and the geometry data 110. By way of example, the processor 104includes a perturbation engine 112 to perturb the geometry data 110associated with the cardiac envelope. Thus, the geometry data 110 forthe cardiac envelope includes data representing a primary geometry forthe cardiac envelope and a perturbed geometry that represents perturbedlocations of nodes distributed across the cardiac envelope. The primarygeometry is the surface onto which the electrical activity isreconstructed. The perturbed geometry corresponds to spatial variationsfor node locations in the primary geometry, such as disclosed herein.The perturbation engine 112 may be programmed to perturb the locationsof the nodes distributed across the cardiac envelope for a set ofperturbations to provide the perturbed geometry data. For example, eachof the perturbations displace nodes on the cardiac envelope according toa perturbation vector, such that each node is moved a known distance anddirection from its initial node location on the cardiac envelope. Theperturbed geometry data may be determined and stored in memory prior tocomputing the reconstructed bipolar electrical signals or perturbedgeometry may be computed as part of such computations. The perturbationengine 112 can provide a number of one or more perturbations, such asrepresenting a set of perturbation vectors for each of the nodes on thecardiac envelope.

A bipolar signal calculator 114 computes reconstructed bipolarelectrical signals on the cardiac envelope based on the electricalsignals at the measurement locations and the geometry data 110,including both the initial and perturbed node locations for the cardiacenvelope. The bipolar signal calculator 114 may compute reconstructedbipolar electrical signals on the cardiac envelope for each perturbationof the geometry data and for each of a plurality of time samples overwhich the electrical data 108 has been obtained by the measurementsystem 106.

As an example, the bipolar signal calculator 114 computes thereconstructed bipolar electrical signals based on a difference betweenthe potential of a given node at the initial location on the cardiacenvelope and the potential of a the given node at each perturbedlocation in the set of perturbations. The resulting bipolar electricalsignals for each of the nodes thus correspond to a bipolar vector havinga magnitude and direction. The magnitude of the bipolar vectorscorresponds to the calculated difference between the potentials at theinitial location and a selected perturbed location. The direction of thebipolar vector for each of the node correspond to a direction along avirtual line extending from the initial node location to the perturbednode location for which the resulting bipolar electrical signal iscomputed. As mentioned, the bipolar electrical signals are computed forthe measured electrical signals over one or more time intervals whichresults in a set of bipolar signal vectors for each respective timesample and for each node on the cardiac envelope.

The bipolar signal calculator 114 selects one of the bipolar vectors foreach node on the cardiac envelope for each time sample. For example, thebipolar signal calculator 114 compares the magnitude of the bipolarvectors computed for the set of perturbation at a given node andrespective time sample to identify a strongest bipolar signal for eachrespective node and time sample. Thus, the number of perturbations willdetermine the accuracy of the resulting bipolar signal for each node.For example, each node on the cardiac envelope will provide a bipolarvector having a magnitude and direction, and the bipolar signalcalculator 114 selects bipolar vector for each node having the largestmagnitude to specify the strongest bipolar signal. The bipolar signalcalculator 114 repeats the comparison for each of the time samples inthe one or more time intervals and thereby identifies a bipolar vectorfor each of the nodes for each of the time samples and the resultingreconstructed electrograms are stored in memory to define bipolarelectrical activity across the cardiac envelope. The repeating of thecomparison for the different nodes and time instances does not requirean iterative process, as the comparisons may be performed concurrently(e.g., in parallel) by respective instances of the bipolar signalcalculator 114.

As a further example, the bipolar signal calculator 114 computes thereconstructed electrical signals according to a bipolar forward modelthat is derived to express electrical activity on the body surface as afunction of a body transformation matrix, such as disclosed herein. Thebipolar signal calculator 114 further solves the inverse computation ofthe bipolar forward model to estimate the bipolar electrical activity onthe cardiac envelope based on the measured body surface electricalactivity provided by the electrical data for the one or more timeintervals. As mentioned, the bipolar signal calculator 114 determinesthe bipolar electrical activity on the cardiac envelope for eachperturbation to provide a corresponding set of reconstructed bipolarsignal vectors for each node on the cardiac envelope. For example, thebipolar signal calculator 114 is further programmed to solve the inversecomputation by regularizing the transformation matrix to estimate aninverse of the bipolar transformation matrix. The bipolar electricalsignals on the cardiac envelope are calculated based on the inversebipolar transformation matrix, which is estimated by suchregularization, and the measured body surface electrical activity storedin the memory 102.

The processor 104 may also include an activation calculator 116 that isprogrammed to compute activation information for the bipolar electricalsignals that have been computed by signal calculator 114 and stored inthe memory 102. The activation calculator may compute an activationtime. Additionally or alternatively, the activation calculator 116 cancompute activation time and direction based on the bipolar signalvectors that have been computed by the bipolar signal calculator 114.The bipolar time and activation thus can be represented as an activationvector, corresponding to the time (vector magnitude) and direction ofactivation for nodes across the cardiac envelope for each beat cycle inthe one or more time intervals represented by the electrical data 108.

The processor 104 can include an output generator 118 that is programmedto generate a graphical map or other outputs 120 that can be provided toa display device 122. For example, the graphical map 120 may include atwo- or three-dimensional visualization of the bipolar electricalsignals computed across the cardiac envelope over one or more timeintervals. Additionally or alternatively, the graphical map 120 mayinclude a graphical representation of the activation time or activationtime and direction (activation vector) across the envelope for each beatcycle in the one or more time intervals for which the measurements havebeen made.

The type of output and the time interval over which the bipolarelectrical signals are reconstructed can be set in response to a userinput provided to the processor 104 via a user interface 124. Forexample, the user interface 124 may include a graphical user interface(GUI) programmed to enable a user to select one or more time intervalsas well as to control the appearance (e.g., viewing angle) of thevisualization provided in the graphical map 120. For example, a user canrotate the 2-D or 3-D graphical map of the cardiac envelope as well asdetermine the type of information that is presented on the map based onthe reconstructed bipolar electrical signals that are computed by theprocessor 104. The resulting graphical map and presentation of bipolarelectrical signals in the output 120 enables an enhancedcharacterization of activation patterns and that are approximate actualbipolar electrograms. Moreover, by perturbing the geometry as part ofthe reconstruction of electrical signals that are being reconstructed,activation direction for each of the nodes across the cardiac envelopeare provided along with the activation time for each bipolar electricalsignal.

FIG. 2 depicts an example of a bipolar signal calculator 200. Thebipolar signal calculator 200 includes a forward computation 202programmed to derive a forward model 204 to express electrical activityon the body surface (e.g., where the measurement system 106 measureselectrical signals on the body surface) as a function of a bipolartransformation matrix (A_(BP)) and the bipolar electrical activity onthe cardiac envelope. By way of example, the electrical activity on thebody surface (Y) may be represented as a function of a transformationmatrix (A₁) and the potential on the cardiac envelope (X₁) for theoriginal initial geometry of the cardiac envelope may be expressed asfollows:

Y=A ₁ *X ₁  (1)

and taking the inverse of (1), may be expressed as follows:

INV(A ₁)Y=INV(A ₁)A1*X ₁ ≈X ₁  (2)

Similarly, the electrical activity on the body surface (y) may berepresented as a function of a transformation matrix (A₂) and thepotential on the cardiac envelope for a given perturbation at adisplaced location (X₂) may be expressed as follows:

Y=A ₂ *X ₂  (3)

and taking the inverse of (3), results in the following:

INV(A ₂)Y=INV(A ₂)A ₂ *X ₂ ≈X ₂.  (4)

By combining the (2) and (4), an estimation of the bipolar electricalactivity may be provided in a combined forward model, as follows:

(INV(A ₁)−INV(A ₂))Y=X ₁ −X ₂  (5)

Let

Y _(new)=INV(INV(A ₁)−INV(A ₂))*(INV(A ₁)−INV(A ₂))Y

and

A _(new)=INV(INV(A ₁)−INV(A ₂))

such that

Y _(new) =A _(new) *ΔX  (6)

The result is a corresponding forward model 204, as demonstrated in (6),which can be utilized to solve the inverse problem and determine thebipolar electrical activity across the cardiac envelope. For example,the bipolar signal calculator 200 also includes an inverse computation206 that uses the forward model 204 to estimate bipolar electricalactivity for nodes on the cardiac envelope based on measured bodysurface electrical activity (Y) for each time sample of a given timeinterval. The inverse computation 206 further can include regularizationthat is applied to estimate the inverse of the bipolar transformationmatrix (A_(BP)) 208, such that the inverse computation 206 can determinethe bipolar electrical activity (X₁−X₂) based on the inverse of thetransformation matrix and the measured body surface electrical activity(Y).

As mentioned, the bipolar signal calculator 200 determines the bipolarelectrical signals across the cardiac envelope for each of a pluralityof perturbations. There can be any number of perturbations, each ofwhich may be displaced a distance and direction from an initial nodelocation. For example, each node can be perturbed according to pluralityof perturbation vectors, such as to define plurality of perturbationnode locations displaced relative to each initial node location.

For example, in FIG. 3, a sphere 300 is shown that includes an initiallocation 302 at a center of the sphere and a plurality of perturbationlocations 304 distributed on the surface of the sphere, where eachperturbation location is spaced an equal distance from the initialcenter location 302. Each perturbation location thus may be representedin three-dimensional space in Cartesian or polar coordinates as aspatial vector having a distance and direction from an initial location.For each perturbation vector that is applied to each node on the cardiacenvelope, a set of bipolar electrical signals (e.g., electrograms) arecomputed for each respective node on the cardiac envelope. Theperturbation locations (e.g., perturbation vectors) can be stored inperturbation data 210 such as in memory 102 of FIG. 1. The perturbationdata thus provides displacement distance and direction for each of theperturbations that are implemented as part of the computation for thebipolar electrical signals by the bipolar signal calculator 200.

As shown in the example of FIG. 3, the set of perturbations can beuniformly distributed in three-dimensional space, such that each node onthe cardiac envelope resides near a center of the set of perturbationsthat are applied for each respective node. In other examples, theperturbation locations may be selected in a desired pattern or randomlyin space. The number and placement of perturbations thus can determinethe accuracy or resolution of the steering function that ultimatelyresults in the strongest bipolar electrical activity for each of theplurality of nodes.

Referring back to FIG. 2, for each perturbation, the bipolar signalcalculator 200 employs the inverse computation 206 with respect to theforward model 204 to compute the bipolar electrical activity at eachnode on the cardiac envelope. The corresponding bipolar electrical datafor each of the perturbations can be stored in memory as bipolar signaldata 212.

Since bipolar electrical activity is computed for each perturbation, thebipolar signal calculator 200 also includes a selector 214 to identifywhich bipolar signal to utilize at a given node for each time sample.For example, the selector 214 includes a comparator 216 that comparesthe magnitude of bipolar vectors computed for the set of perturbationsat a given a node and given time sample to identify a strongest bipolarsignal for such given node and given time sample. Thus the comparator216 can identify the strongest bipolar signals and its direction acrossthe cardiac envelope for time samples in one or more time intervals forwhich electrical measurements have been acquired (e.g., on the surfaceof the patient's torso). By utilizing multiple perturbations andidentifying the strongest bipolar signal for each node, steerability isimplemented and captured in the resulting bipolar electrical signalsthat are identified for each node on the cardiac envelope.

The selector 214 is configured to repeat the comparison for each of thetime samples and the time interval (or intervals) to identify thestrongest bipolar electrical signal for each of the nodes in each of thetime samples. The strongest identified bipolar signals for each node andeach time sample are stored in memory to provide correspondingreconstructed bipolar electrical signals for the time interval which canbe stored in memory as the bipolar signal data 212.

FIG. 4 depicts an example of cardiac envelopes 400 showing an initialcardiac envelope 402 and a perturbed cardiac envelope 404 that isdisplaced a given direction and orientation relative to the initiallocation (e.g., according to a perturbation vector, such as shown inFIG. 3). The number and location of the perturbations of the cardiacenvelope can vary according to application requirements. In the exampleof FIG. 4, the cardiac envelope 402 is demonstrated as a triangular meshsurface corresponding to an epicardial surface of a heart. Vertices(e.g., nodes) of the mesh correspond to locations on the cardiacenvelope for which electrical activity is being reconstructed. Thus, thelocations in 402 correspond to an initial cardiac envelope and thelocations in 404 correspond to perturbed locations that have beendisplaced according to application of a perturbation vector to theinitial locations. Accordingly, a calculated difference between theelectrical signal for each of the locations on the first envelope 402and respective (perturbed) locations on the second envelope 404 providesa bipolar signal value for each of the respective locations on thecardiac envelope. As disclosed herein, bipolar signals can similarly becomputed over a plurality of perturbation vectors and over one or moretime intervals, each containing plurality of time samples.

In view of the foregoing structural and functional features describedabove, methods in accordance with various aspects of the presentdisclosure will be better appreciated with reference to FIGS. 5 and 6.While, for purposes of simplicity of explanation, the method of FIGS. 5and 6 are shown and described as executing serially, it is to beunderstood and appreciated that the present disclosure is not limited bythe illustrated orders, as some aspects could, in other examples, occurin different orders and/or concurrently from that shown and describedherein. Moreover, not all illustrated features may be required toimplement the methods.

FIG. 5 depicts an example to determine bipolar electro activity. Themethod 500 begins at 502 in which geometry data is stored. Electricaldata can also be stored at 502, which may include electrical data thatwas previously measured from the body surface or is being measuredintraprocedurally (e.g., in real time). For example, the data may bestored in memory storage device, such as random access memory (RAM, suchas static RAM or dynamic RAM), a solid state drive, hard disk drivestorage, which may be internal or external to the computer implementingthe method 500.

At 504, the geometry is perturbed. The geometry may include a cardiacenvelope onto which the electrical activity (e.g., electrophysiologicalsignals, such as electrograms) is being reconstructed. For example, thecardiac envelope onto which the electrical activity is beingreconstructed can be perturbed according to a perturbation vector. Thus,at 504, each of a plurality of nodes on the cardiac envelope may bedisplaced from an original location on the cardiac envelope to itsperturbed location by a given distance at a given direction defined bythe perturbation vector.

At 506, reconstructed bipolar electrical signals are computed. Asdisclosed herein, the bipolar electrical signals can be reconstructedonto the cardiac envelope based on the geometry data and the measuredbody surface electrical activity. For example, the reconstructed bipolarelectrical signals can be determined on the by solving the inverseproblem as a function of a difference between the reconstructedelectrical signal value for each original location on the cardiacenvelope and the electrical signal value at each respective perturbedlocation on the cardiac envelope.

At 508, a determination is made as to whether more perturbations are tobe implemented. If more perturbations exist, the method returns from 508to 504 in which the geometry is perturbed again. The perturbation can befrom the initial location or the perturbation can be perturbed accordingto a vector from the previously perturbed location until the set ofperturbations, which may be predetermined or determined based on thevalues of bipolar electrical signals as computed at 506, have beencompleted at 508. From 508 after no additional perturbations are to beperformed the method proceeds to 510.

At 510 the strongest bipolar signals across the geometry are identified.For example, the bipolar signals determined at a given time instance foreach node on the cardiac envelope for the set of perturbations areevaluated and compared. The strongest bipolar electrical signal computedfor each node on the envelope at the given time instance (from thosecomputed from the set of perturbations) can be tagged to define thebipolar signals for the cardiac envelope for the given time instance.The identified bipolar signals can be aggregated for each time instancestored in memory. The corresponding bipolar electrical signals can beaggregated over a series of time instances (to provide reconstructedbipolar electrical signals across the cardiac envelope over one or moretime intervals such as disclosed herein.

FIG. 6 is a flow diagram depicting an example method for computingbipolar electrical signals. The method 600 begins at 602 in whichgeometry and electrical data is stored. As disclosed herein, thegeometry data may represent geometry of electrodes distributed acrossthe body surface of a patient as well as geometry of patient anatomy(i.e., actual geometry or a model). The electrical data may correspondto electrical data that is being measured in real time and/or data thathas previously acquired. At 604, a bipolar forward model is derived. Forexample, the forward model represents the electrical signals on the bodysurface as a function of a transformation matrix and bipolar potentialsdistributed on a cardiac envelope, such as disclosed herein (see, e.g.,Equation 5 or 6).

At 606, an inverse solution is derived to express bipolar electricalactivity. For example, the inverse solution is utilized forreconstructing bipolar electrical signals on the cardiac envelope asdisclosed herein. The cardiac envelope can correspond to an actualsurface of the heart, or any cardiac envelope that resides within thebody beneath the body surface on which the electrical activity ismeasured. The inverse problem is ill-posed. Accordingly, at 608 theinverse of the bipolar transformation matrix is estimated. Theestimation may be performed by various regularization techniques, suchas Tikhonov regularization or calculating a method of fundamentalsolution. Examples of these regularization techniques that may beimplemented at 608 are disclosed in the above-incorporated U.S. Pat.Nos. 6,772,004 and 7,983,743. In other examples, other regularizationmethods may be utilized, such as Lasso, elastic net and total variationmethods. For example, the inverse computation may be applied directly toEq. 5.

At 610, bipolar electrical activity is determined on the geometry ofinterest, such as the cardiac envelope. The bipolar electrical activityfor each of the nodes may be determined according to the approachdescribed with respect to FIG. 5, such as where computations of bipolarelectrical signals for each node over a set of perturbations is used tosteer the method to identify a strongest bipolar electrical signal foreach node.

At 612, a determination is made as to whether there are additional timesamples of the electrical data. If more time samples exist, the methodreturns from 612 to 604 to determine the bipolar electrical activity forthe next time sample. The derivations at 604 and 606 may utilize thesame system of equations, but include the new values of electricalsignals on the body surface for the next time sample for estimatingbipolar electrical activity. If it is determined that no more samplesexist at 612, the method can proceed to 614 to store the bipolar signaldata in suitable memory.

FIG. 7 depicts an example of a system 700 that can be utilized forperforming diagnostics and/or treatment of a patient 704. In someexamples, the system 700 can assess of the heart 702 in real time(intraprocedurally) as part of a diagnostic and/or treatment procedure,such as to help a physician deliver a therapy to the patient 704 (e.g.,delivery location, amount and type of therapy).

In the example of FIG. 7, a sensor array 706 includes one or moreelectrodes that can be utilized for recording patient activity. As oneexample, the sensor array 706 can correspond to a high-densityarrangement of body surface sensors (e.g., greater than 200 electrodes)that are distributed over a portion of the patient's thorax formeasuring electrical activity associated with the patient's heart (e.g.,as part of an electrocardiographic mapping procedure). Examples of anon-invasive sensor array that can be used as the sensor array are shownand described in the above-incorporated U.S. Pat. No. 9,655,561 and inInternational patent application No. PCT/US2009/063803. Otherarrangements and configurations of sensing electrodes can be used as thesensor array 706 in other examples. The array 706 can be a reduced setof electrodes, which that does not cover the patient's entire torso andis designed for measuring electrical activity for a particular purpose(e.g., an array of electrodes specially designed for analyzing AF and/orVF).

In some examples, one or more sensors may also be located on a device708 that is configured for insertion into the patient's body via a lowor minimally invasive procedure (e.g., an electrophysiology (EP) study).In some examples, the device 708 can be utilized in conjunction with thesensor array 706 for mapping (e.g., by mapping system 712) electricalactivity for an endocardial surface such as the wall of a heart chamber.Additionally or alternatively, the device 708 can be localized withinthe heart 702, which can be registered into an image or map that isgenerated by the system 700.

The sensor array 706 includes electrodes coupled (e.g., via electricallyconductive wires or traces) to provide the sensed electrical information(e.g., electrical potential measurements) to a corresponding measurementsystem 716. The measurement system 716 can include appropriate controlsand signal processing circuitry 718 for providing correspondingmeasurement data 720 that describes electrical activity measured by thesensors in the sensor array 714. The measurement data 720 can includeanalog or digital information.

The control 718 can also be configured to control the data acquisitionprocess (e.g., sampling rate) for measuring electrical activity andproviding the measurement data 720 over time. The measurement data 720can be acquired concurrently in the absence of or in conjunction withthe delivering therapy by the therapy system, such as to detectelectrical activity of the heart 702 that occurs in response to applyinga given therapy (e.g., according to therapy parameters).

The mapping system 712 is programmed to combine the measurement data720, corresponding to body surface electrical activity (e.g., electricalpotential measurements) from the heart 702, with geometry data 722 byapplying appropriate processing and computations (e.g., as disclosedwith respect to FIGS. 1-6) to generate a corresponding output. Forinstance, the mapping system 712 can generate output data including agraphical representation of bipolar electrical activity across thecardiac envelope (e.g., on a surface of the heart 702). The output datacan be supplied to a graphics pipeline and provided to a display 724 viaa display interface (not shown).

The mapping system 712 includes signal processing methods programmed to,when executed by one or more processors, transform the geometry data 722and measurement data 720 into actionable health information. In theexample of FIG. 7, the signal processing methods include a bipolarelectrogram reconstruction method 726, an activation data calculator 728and a map generator 730. For example, the bipolar electrogramreconstruction method 726 is programmed to compute reconstructed bipolarelectrical signals on a cardiac envelope based on the electricalmeasurement data 720 and the geometry data 722. As disclosed herein, thebipolar reconstruction method may perturb geometry of the cardiacenvelope for a set of perturbations (according to a perturbation vector)at each of a plurality of time samples. The bipolar electrogramreconstruction method 726 thus may solve the inverse problem to estimatethe bipolar electrograms for the nodes on the cardiac envelope (e.g., asdefined by the geometry data) for each perturbation for each of theplurality of time samples. The magnitude of bipolar electrogramscomputed for each of the perturbations at a given node and time sampleare compared with respect to each other to identify a strongest bipolarsignal for such given node and time sample. The comparison is thusrepeated for each of the time samples to identify the strongest bipolarsignal for each of the nodes on the cardiac envelope (e.g., the heartsurface) at each of the time samples.

The activation data calculator 728 further can compute activation timefor the nodes on the cardiac envelope from the reconstructed bipolarelectrical signals. The activation data calculator 728 may compute anactivation time. Additionally or alternatively, the activationcalculator 116 can compute activation time and direction based onbipolar signal vectors that have been computed by the bipolar signalcalculator. That is, since each bipolar signal is derived from a pair oflocations (a node on the cardiac envelope and a perturbed location), theresulting bipolar signal can be provided as a vector having bothmagnitude and direction.

The map generator 730 is programmed to generate a graphical map based onthe reconstructed bipolar electrical signals, which may be a static mapor dynamic map that varies as a function of time (e.g., stepping throughtime instances of one or more time intervals. The map generator 730 mayalso generate the graphical map to display one or both of activationtime and direction of activation corresponding to the reconstructedbipolar electrical signals computed over a time interval. Since themeasurement system 716 can, in some examples, measure electricalactivity for the entire heart concurrently, the resulting output data(e.g., bipolar electrograms or derivations thereof) thus can alsorepresent concurrent data for the heart in a temporally and spatiallyconsistent manner. The time interval for which the output data/maps arecomputed can be selected based on user input. Additionally oralternatively, the selected intervals can be synchronized with theapplication of therapy by the therapy system 708.

As disclosed herein, the geometry data 722 may representthree-dimensional geometry of the patient's torso (where the sensorarray 706 is positioned) and the cardiac envelope to where the bipolarelectrograms are being reconstructed. For example, the geometry data 722may be derived from and/or including image data acquired for thepatient. Such image processing can include extraction and segmentationof anatomical features, including one or more organs and otherstructures, from a digital image set. Additionally, a location for eachof the electrodes in the sensor array 706 can be included in thegeometry data 722, such as by acquiring the image while the electrodesare disposed on the patient and identifying the electrode locations in acoordinate system through appropriate extraction and segmentation. Theresulting segmented image data can be converted into a two-dimensionalor three-dimensional graphical representation that includes the regionof interest for the patient.

By way of further example, the geometry data 722 can be acquired usingnearly any imaging modality (e.g., x-ray, ultrasound, computedtomography, magnetic resonance imaging, or the like) based on which acorresponding representation can be constructed, such as describedherein. Such imaging may be performed concurrently with recording theelectrical activity that is utilized to generate the patient electricalmeasurement data 720 or the imaging can be performed separately (e.g.,before the measurement data has been acquired).

Alternatively, the geometry data 722 can correspond to a mathematicalmodel, such as can be a generic model or a model that has beenconstructed based on image data for the patient. Appropriate anatomicalor other landmarks, including locations for the electrodes in the sensorarray 714 can be identified in the geometry data 722 to facilitateregistration of the electrical measurement data 720 and computing theinverse solution for reconstructing bipolar electrograms thereon. Theidentification of such landmarks can be done manually (e.g., by a personvia image editing software) or automatically (e.g., via image processingtechniques).

As a further example, a catheter, such as a pacing catheter or ablationcatheter, having one or more therapy delivery devices 708 affixedthereto can be inserted into the body 704 as to contact the patient'sheart 702 at one or more locations, endocardially or epicardially. Thoseskilled in the art will understand and appreciate various type andconfigurations of therapy delivery devices 708 that can be utilized,which can vary depending on the type of treatment and the procedure.

For example, the therapy device 708 can include one or more electrodeslocated at a tip of an ablation catheter configured to generate heat forablating tissue in response to electrical signals (e.g., radiofrequencyenergy) supplied by a therapy system 710. In other examples, the therapydelivery device 708 can be configured to deliver cooling to performablation (e.g., cryogenic ablation), to deliver chemicals (e.g., drugs),ultrasound ablation, high-frequency ablation, or a combination of theseor other ablation mechanisms. In still other examples, the therapydelivery device 708 can include one or more electrodes located at a tipof a pacing catheter to deliver electrical stimulation, such as forpacing the heart, in response to electrical signals (e.g., pacingpulses) supplied by a therapy system 710. Other types of therapy canalso be delivered via the therapy system 708 and the invasive therapydelivery device 708 that is positioned within the body 704.

The therapy system 710 can be located external to the patient's body 704and be configured to control therapy that is being delivered by thedevice 708. For instance, the therapy system 710 includes controlcircuitry 732 that can communicate (e.g., supply) electrical signals viaa conductive link electrically connected between the device (e.g.,electrodes) 708 and the therapy system 710. The control system 732 cancontrol parameters of the signals supplied to the device 708 (e.g.,current, voltage, repetition rate, trigger delay, sensing triggeramplitude) for delivering therapy (e.g., ablation or stimulation) viathe device(s) 7084 to one or more location of the heart 702.

The control circuitry 732 can set the therapy parameters and applystimulation based on automatic, manual (e.g., user input) or acombination of automatic and manual (e.g., semiautomatic controls). Oneor more sensors (not shown) can also communicate sensor information backto the therapy system 708. In some examples, the mapping system 712communicates with the therapy system 710 through a physical or wirelesslink. In this way the mapping system 712 may supply control information(e.g., commands), such as derived from reconstructed bipolar electricalsignals, to the therapy system 710. The therapy system 710 thus caninterpret the control information to control delivery of the therapy tothe heart 702 via the device 708. Additionally or alternatively, such aswhere the device 708 includes one or more sensors (e.g., sensingelectrodes), the therapy system 710 can communicate electricalmeasurement data for cardiac electrical activity measured directly fromthe heart.

FIGS. 8 and 9 depict examples of epicardial graphical maps 800 and 900that may be generated (e.g., by output generator 118 or map generator730) to visualize reconstructed bipolar electrograms. FIG. 8 depicts anexample of a graphical map 800 to display activation time across atank-torso model based on reconstructed bipolar electrical signalsgenerated according to this disclosure. The graphical map in FIG. 9depicts both activation time and activation direction derived fromreconstructed bipolar electrical signal (which include bipolar vectorshaving both magnitude and direction). The example maps of bipolarelectrical activity shown in FIGS. 8 and 9 afford greater accuracycompared to similar approaches that may generate maps based on unipolarelectrical signals. For instance, bipolar epicardial electrogramsreconstructed, as disclosed herein, have higher correlation coefficient(e.g., measuring the morphology accuracy) and smaller relative error(e.g., measuring magnitude accuracy) compared to the many existingunipolar reconstructed epicardial electrograms.

In view of the foregoing structural and functional description, thoseskilled in the art will appreciate that portions of the invention may beembodied as a method, data processing system, or computer programproduct. Accordingly, these portions of the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, or an embodiment combining software and hardware.Furthermore, portions of the invention may be a computer program producton one or more computer-usable storage media having computer readableprogram code on such media. Any suitable computer-readable medium may beutilized including, but not limited to, static and dynamic storagedevices, hard disks, optical storage devices, and magnetic storagedevices.

Certain embodiments of the invention have also been described hereinwith reference to block illustrations of methods, systems, and computerprogram products. It will be understood that blocks of theillustrations, and combinations of blocks in the illustrations, can beimplemented by computer-executable instructions. Thesecomputer-executable instructions may be provided to one or moreprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus (or a combination ofdevices and circuits) to produce a machine, such that the instructions,which execute via the processor, implement the functions specified inthe block or blocks.

These computer-executable instructions may also be stored incomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory result in an article of manufacture including instructions whichimplement the function specified in the flowchart block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

What have been described above are examples. It is, of course, notpossible to describe every conceivable combination of components ormethods, but one of ordinary skill in the art will recognize that manyfurther combinations and permutations are possible. Accordingly, theinvention is intended to embrace all such alterations, modifications,and variations that fall within the scope of this application, includingthe appended claims. Additionally, where the disclosure or claims recite“a,” “an,” “a first,” or “another” element, or the equivalent thereof,it should be interpreted to include one or more than one such element,neither requiring nor excluding two or more such elements. As usedherein, the term “includes” means includes but not limited to, and theterm “including” means including but not limited to. The term “based on”means based at least in part on.

What is claimed is:
 1. One or more computer-readable media havingcomputer-executable instructions for performing a method comprising:storing geometry data representing a primary geometry of a cardiacenvelope that includes nodes distributed across the cardiac envelope andgeometry of a body surface that includes locations where electricalsignals are measured, the body surface being spaced apart from thecardiac envelope; perturbing the primary geometry of the cardiacenvelope a given distance and direction to define a perturbed geometryof the cardiac envelope including nodes spaced from the nodes of theprimary geometry; and computing reconstructed bipolar electrical signalson the nodes of the primary cardiac envelope based on the electricalsignals measured from the body surface and the geometry data, includingthe primary and perturbed geometries of the cardiac envelope.
 2. Themedia of claim 1, wherein the method further comprise storing electricaldata representing the measured electrical signals on the body surfaceover at least one time interval, the reconstructed bipolar electricalsignals being computed on the cardiac envelope for each of a pluralityof time samples during the time interval.
 3. The media of claim 2,wherein the nodes of the cardiac envelope are perturbed for a set ofperturbations at each of the plurality of time samples, eachperturbation in the set of perturbations displacing the nodes of thecardiac envelope a known distance and direction from the respectivenodes of the primary geometry, the reconstructed bipolar electricalsignals are computed for each perturbation in the set of perturbationsfor each of the plurality of time samples.
 4. The media of claim 3,wherein computing reconstructed bipolar electrical signals includescalculating a difference between an electrical potential of a given nodeof the primary geometry of the cardiac envelope and an electricalpotential of the given node at each perturbed node location of thecardiac envelope.
 5. The media of claim 4, wherein each of thereconstructed bipolar electrical signals for each of nodes on thecardiac envelope is computed as a bipolar vector having a magnitude anddirection, the magnitude corresponding to the calculated difference andthe direction corresponding to a direction along a virtual lineextending from a node location of the primary geometry to thecorresponding perturbed node location in the corresponding perturbedgeometry.
 6. The media of claim 5, wherein the method further comprises:comparing the magnitude of the bipolar vectors computed for the set ofperturbations at the given node and time sample to identify a strongestbipolar signal for the given node and time sample; and repeating thecomparison for each of the time samples in the time interval to identifythe strongest bipolar signal for each of the nodes in each of the timesamples, the strongest bipolar signal for each of the nodes in each ofthe time samples being stored in memory as the reconstructed bipolarelectrical signals on the primary geometry of the cardiac envelope forthe time interval.
 7. The media of claim 2, wherein the set ofperturbations are uniformly distributed in three-dimensionalperturbation space such that each respective node of the primarygeometry of the cardiac envelope resides near a center of theperturbation space for the respective node.
 8. The media of claim 1,wherein computing the reconstructed bipolar electrical signals furthercomprises: deriving a bipolar forward model expressing the electricalsignals on the body surface as a function of a bipolar transformationmatrix and the reconstructed bipolar electrical signals on the primarygeometry of the cardiac envelope; and solving an inverse computation ofthe bipolar forward model to determine the reconstructed bipolarelectrical signals on the primary geometry of the cardiac envelope basedon the electrical signals measured from the body surface.
 9. The mediaof claim 8, wherein solving the inverse computation further comprisesregularizing the bipolar transformation matrix to estimate an inverse ofthe bipolar transformation matrix, the reconstructed bipolar electricalsignals on the cardiac envelope being determined based on the inverse ofthe bipolar transformation matrix and the electrical signals measuredfrom the body surface.
 10. The media of claim 8, wherein the nodes ofthe cardiac envelope are perturbed for a set of perturbations at each ofa plurality of time samples, each perturbation displacing the primarygeometry of the cardiac envelope a respective distance and direction todefine the perturbed geometry of the cardiac envelope, such that thenodes of the perturbed geometry are spaced from the nodes of the primarygeometry, wherein the reconstructed bipolar electrical signals arecomputed for each perturbation in the set of perturbations for each ofthe plurality of time samples as a bipolar vector, having a magnitudeand direction, based on a difference between an electrical potential ofeach respective node of the primary geometry and an electrical potentialof its corresponding node of the perturbed geometry.
 11. The media ofclaim 10, wherein for each node of the primary geometry of the cardiacenvelope, the method further comprising: comparing a magnitude of thebipolar vector computed for each respective node over the set ofperturbations to identify a strongest bipolar signal for each respectivenode; and storing the strongest bipolar signal for each of the nodes asthe reconstructed bipolar electrical signal for the respective node. 12.The media of claim 1, wherein the method further comprises: generating agraphical map that displays at least one of the activation time anddirection of activation corresponding to the reconstructed bipolarelectrical signals computed over a time interval.
 13. The media of claim1, wherein the measured electrical signals comprise unipolarelectrograms measured non-invasively using a plurality of body surfaceelectrodes distributed on a portion patient's torso.
 14. A system,comprising: memory to store instructions and data, the data comprising:electrical data representing electrical signals measured on a bodysurface for at least one time interval; and geometry data representing aprimary geometry of a cardiac envelope, including nodes distributedacross the cardiac envelope, and geometry of a body surface includingmeasurement locations for the electrical signals measured on the bodysurface; a processor to access the memory and execute the instructionsto at least: compute reconstructed bipolar electrical signals on tonodes residing on the primary geometry of the cardiac envelope based onthe electrical data and the geometry data, including the primarygeometry and a perturbed geometry of the cardiac envelope.
 15. Thesystem of claim 14, wherein the instructions are further programmed toperturb the primary geometry of the cardiac envelope a given distanceand direction to define the perturbed geometry of the cardiac envelope.16. The system of claim 15, wherein the instructions are furtherprogrammed to perturb the nodes of the cardiac envelope for a set ofperturbations at each of a plurality of time samples, each perturbationin the set of perturbations displacing the nodes of the primary geometrya known distance and direction from the respective nodes to providecorresponding perturbed node locations, the reconstructed bipolarelectrical signals are computed for each perturbation in the set ofperturbations for each of the plurality of time samples.
 17. The systemof claim 16, wherein the reconstructed bipolar electrical signals arecomputed based on a potential difference between an electrical potentialof a given node of the primary geometry of the cardiac envelope and anelectrical potential at the respective perturbed node location.
 18. Thesystem of claim 17, wherein the reconstructed bipolar electrical signalscomputed for each of nodes is computed as a bipolar vector having amagnitude and direction, the magnitude corresponding to the potentialdifference and the direction corresponding to a direction along avirtual line extending from a node location of the primary geometry tothe respective perturbed node location.
 19. The system of claim 18,wherein the instructions are further programmed to: compare themagnitude of the bipolar vectors computed for the set of perturbationsat the given node and time sample to identify a strongest bipolar signalfor the given node and time sample, wherein the comparison is performedfor each of the time samples in a time interval to identify thestrongest bipolar signal for each of the nodes in each of the timesamples, the strongest bipolar signal for each of the nodes in each ofthe time samples being stored in memory as the reconstructed bipolarelectrical signals for the primary geometry of the cardiac envelope overthe time interval.
 20. The system of claim 16, wherein the set ofperturbations are uniformly distributed in three-dimensionalperturbation space such that each respective node of the primarygeometry of the cardiac envelope resides near a center of theperturbation space for the respective node.
 21. The system of claim 14,further comprising an arrangement of sensors configured tonon-invasively measure the electrical signals from the body surface,wherein the electrical signals comprise unipolar electrograms.
 22. Thesystem of claim 14, wherein the instructions for computing thereconstructed bipolar electrical signals are further programmed to:derive a bipolar forward model expressing the electrical signals on thebody surface as a function of a bipolar transformation matrix and thereconstructed bipolar electrical signals on the primary geometry of thecardiac envelope; and determine a solution to an inverse computation ofthe bipolar forward model to determine the reconstructed bipolarelectrical signals on the primary geometry of the cardiac envelope basedon the electrical signals measured from the body surface.
 23. The systemof claim 22, wherein the solution to the inverse computation furthercomprises regularizing the bipolar transformation matrix to estimate aninverse of the bipolar transformation matrix, the reconstructed bipolarelectrical signals on the primary geometry of the cardiac envelope beingdetermined based on the inverse of the bipolar transformation matrix andthe electrical signals measured from the body surface.
 24. The system ofclaim 22, wherein the nodes of the cardiac envelope are perturbed for aset of perturbations at each of a plurality of time samples, eachperturbation displacing the primary geometry of the cardiac envelope agiven distance and direction to define the perturbed geometry of thecardiac envelope, such that the nodes of the perturbed geometry arespaced from the nodes of the primary geometry, wherein the reconstructedbipolar electrical signals are computed for each perturbation in the setof perturbations for each of the plurality of time samples as a bipolarvector, having a magnitude and direction, based on a difference betweenan electrical potential of each respective node of the primary geometryand an electrical potential of its corresponding node of the perturbedgeometry.
 25. The system of claim 24, wherein, for each node of theprimary geometry of the cardiac envelope, the instructions are furtherprogrammed to: compare a magnitude of the bipolar vector computed foreach respective node over the set of perturbations to identify astrongest bipolar signal for each respective node; and store thestrongest bipolar signal for each of the nodes as the reconstructedbipolar electrical signal for the respective node.
 26. The system ofclaim 14, wherein the instructions are further programmed to generate agraphical map representing at least one of activation time and directionof activation corresponding to the reconstructed bipolar electricalsignals, the system further comprising a display device configured todisplay the graphical map.